Para ilmuwan memotong kerusakan otak dengan re - encoding kenangan--T-REC-komunitas reptil-semarang--KSE-komunitas satwa eksotik--memori-otak--berita artikel tentang otak dan kerusakan pada otak

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Para ilmuwan  memotong kerusakan otak dengan  re - encoding kenangan

Prostesis baru ini bertujuan untuk membantu orang yang hidup dengan kehilangan memori

Date:

September 29, 2015

Source:

University of Southern California

Summary:

Para peneliti menguji prostesis yang menerjemahkan kenangan jangka pendek menjadi lebih jangka panjang, dengan potensi untuk memotong bagian yang rusak dari otak .

....... prostesis , yang mencakup array kecil elektroda ditanamkan ke otak , memiliki kinerja yang baik dalam pengujian laboratorium pada hewan dan saat ini sedang dievaluasi pada pasien manusia .

Dirancang awalnya di USC dan diuji di Wake Forest Baptist , perangkat dibangun pada dekade penelitian oleh Ted Berger dan bergantung pada algoritma baru yang diciptakan oleh Dong Song ,  dari USC Viterbi School of Engineering .....more

Scientists to
bypass brain damage by re-encoding memories

New prosthesis aims to help people living with memory loss

Date:

September 29, 2015

Source:

University of Southern California

Summary:

Researchers are testing a prosthesis that translates short-term memories
into longer-term ones, with the potential to bypass damaged portions of the
brain.

....................

Researchers at USC and Wake Forest Baptist Medical Center have developed a
brain prosthesis that is designed to help individuals suffering from memory
loss.

The prosthesis, which includes a small array of electrodes implanted into
the brain, has performed well in laboratory testing in animals and is currently
being evaluated in human patients.

Designed originally at USC and tested at Wake Forest Baptist, the device
builds on decades of research by Ted Berger and relies on a new algorithm
created by Dong Song, both of the USC Viterbi School of Engineering. The
development also builds on more than a decade of collaboration with Sam Deadwyler
and Robert Hampson of the Department of Physiology & Pharmacology of Wake
Forest Baptist who have collected the neural data used to construct the models
and algorithms.

When your brain receives the sensory input, it creates a memory in the form
of a complex electrical signal that travels through multiple regions of the
hippocampus, the memory center of the brain. At each region, the signal is
re-encoded until it reaches the final region as a wholly different signal that
is sent off for long-term storage.

If there's damage at any region that prevents this translation, then there
is the possibility that long-term memory will not be formed. That's why an
individual with hippocampal damage (for example, due to Alzheimer's disease)
can recall events from a long time ago -- things that were already translated
into long-term memories before the brain damage occurred -- but have difficulty
forming new long-term memories.

Song and Berger found a way to accurately mimic how a memory is translated
from short-term memory into long-term memory, using data obtained by Deadwyler
and Hampson, first from animals, and then from humans. Their prosthesis is
designed to bypass a damaged hippocampal section and provide the next region
with the correctly translated memory.

That's despite the fact that there is currently no way of
"reading" a memory just by looking at its electrical signal.

"It's like being able to translate from Spanish to French without
being able to understand either language," Berger said.

Their research was presented at the 37th Annual International Conference of
the IEEE Engineering in Medicine and Biology Society in Milan on August 27,
2015.

The effectiveness of the model was tested by the USC and Wake Forest
Baptist teams. With the permission of patients who had electrodes implanted in
their hippocampi to treat chronic seizures, Hampson and Deadwyler read the
electrical signals created during memory formation at two regions of the
hippocampus, then sent that information to Song and Berger to construct the model.
The team then fed those signals into the model and read how the signals
generated from the first region of the hippocampus were translated into signals
generated by the second region of the hippocampus.

In hundreds of trials conducted with nine patients, the algorithm
accurately predicted how the signals would be translated with about 90 percent
accuracy.

"Being able to predict neural signals with the USC model suggests that
it can be used to design a device to support or replace the function of a damaged
part of the brain," Hampson said.

Next, the team will attempt to send the translated signal back into the
brain of a patient with damage at one of the regions in order to try to bypass
the damage and enable the formation of an accurate long-term memory.







Story Source:

The above post is reprinted from materials provided byUniversity
of Southern California. The original item was written by Robert
Perkins. Note: Materials may be edited for content and length.

http://www.sciencedaily.com/releases/2015/09/150929142524.htm

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